RESEARCH & RESOURCES

Ten Mistakes to avoid when developing a data quality strategy cover image

Ten Mistakes to Avoid When Implementing a Data Quality Strategy

July 13, 2018

 

By Patty Haines

Data quality is essential to getting more value from your organization’s data assets. Analysts, data scientists, and managers must know and understand the quality of the data they are using to make decisions and to set direction for their organizations if they are to make the best decisions.

Today, data is created and collected by every type of device used in businesses and homes. Data analytics has become a common term. Expectations for quality data continue to grow at an increasing rate.

However, implementing a data quality strategy is not as simple as installing a tool or a one-time fix. Organizations across the enterprise need to work together to identify, assess, remediate, and monitor data with the goal of continual data improvements.


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